A Neural Network Model for Attribute-Based Decision Processes

We propose a neural model of multiattribute-decision processes, based on an attractor neural network with dynamic thresholds. The model may be viewed as a generalization of the elimination by aspects model, whereby simultaneous selection of several aspects is allowed. Depending on the amount of synaptic inhibition, various kinds of scanning strategies may be performed, leading in some cases to vacillations among the alternatives. The model predicts that decisions of a longer time duration exhibit a lower violation of the simple scalability law, as opposed to shorter decisions. Furthermore, the model is suggested as a general attribute-based decision module. Accordingly, various decision strategies are manifested depending on the module's parameters.     

The Drugs May Be Synthetic but the Consequences are not:Analysis of Synthetic Cannabinoid User Accounts

ABSTRACT

Synthetic cannabinoids are the second most used illicit substance among U.S. adolescents. Despite their prevalence and dangers, we know little about the characteristics associated with use. While clinical samples have shed some light on how synthetic cannabinoids differ from marijuana, they have not explored career trajectories of synthetic cannabinoid users. We analyze online forum posts about people’s synthetic cannabinoid use to understand more about these novel psychoactive drugs and the drug careers of those who use.     

Inter-group alliance dynamics in Indo-Pacific bottlenose dolphins (Tursiops aduncus)

Animal Cognition - The social intelligence hypothesis holds that complex social relationships are the major selective force underlying the evolution of large brain size and intelligence. Complex...